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Search Results: 1 - 10 of 12666 matches for " clipped LMS algorithm "
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Modified Clipped LMS Algorithm
Mojtaba Lotfizad,Hadi Sadoghi Yazdi
EURASIP Journal on Advances in Signal Processing , 2005, DOI: 10.1155/asp.2005.1229
Abstract: A new algorithm is proposed for updating the weights of an adaptive filter. The proposed algorithm is a modification of an existing method, namely, the clipped LMS, and uses a three-level quantization (+1,0, ¢ ’1) scheme that involves the threshold clipping of the input signals in the filter weight update formula. Mathematical analysis shows the convergence of the filter weights to the optimum Wiener filter weights. Also, it can be proved that the proposed modified clipped LMS (MCLMS) algorithm has better tracking than the LMS algorithm. In addition, this algorithm has reduced computational complexity relative to the unmodified one. By using a suitable threshold, it is possible to increase the tracking capability of the MCLMS algorithm compared to the LMS algorithm, but this causes slower convergence. Computer simulations confirm the mathematical analysis presented.
Performance Variation of LMS And Its Different Variants
Sanjeev Dhull,Dr.Sandeep Arya,Dr.O.P Sahu
International Journal of Computer Science and Security , 2010,
Abstract: Acoustic echo cancellation is an essential and important requirement for various applications such as, telecasting, hands,free telephony and video,conferencing. Echo cancellers are required because of loud,speaker signals are picked up by a microphone and are fed back to the correspondent, resulting in an undesired echo. These days, adaptive filtering methods are used to cancel the affect of these echoes. Different variants of LMS adaptive algorithms have been. Implemented and they are compared based upon their performance according to the choice of step size.
A New Method for Sensing Cognitive Radio Network under Malicious Attacker  [PDF]
Shaahin Tabatabaee, Vahid Tabataba Vakili
Int'l J. of Communications, Network and System Sciences (IJCNS) , 2013, DOI: 10.4236/ijcns.2013.61007

Cognitive radio has been designed for solving the problem of spectrum scarcity by using the spectrum of primary users who don’t use their spectrum on that time. For sensing the spectrum, collaborative spectrum sensing has been utilized because of robustness. In this paper, a new collaborative spectrum method is proposed based on Least Mean Square (LMS) algorithm. In this scheme, the weights of secondary users were updated in time and finally the sensing results were combined in the fusion center based on their trusted weights. Simulation results show that the proposed scheme can significantly reduce the effects of Spectrum Sensing Data Falsification (SSDF) attackers, when they are smart malicious, and even percentage of malicious users are more than trusted users.

Performance Evaluation Of Different Adaptive Filters For ECG Signal Processing
Sachin singh,,Dr K. L. Yadav
International Journal on Computer Science and Engineering , 2010,
Abstract: One of the main problem in biomedical data processing like electrocardiography is the separation of the wanted signal from noises caused by power line interference, external electromagnetic fields and random body movements and respiration. Different types of digital filters are used to remove signal components from unwanted frequency ranges. It is difficult to apply filters with fixed coefficients to reduce Biomedical Signal noises, because human behavior is not exact known depending on the time. Adaptive filter technique is required to overcome this problem. In this paper two types of adaptive filters are considered to reduce the ECG signal noises like PLI and Base Line Interference. Results of simulations inMATLAB are presented.
Beam Forming Algorithm Implementation using FPGA
Arathy Reghu kumar,K. P Soman,Shanmuga Sundaram G.A
International Journal of Advanced Electrical and Electronics Engineering , 2013,
Abstract: In this paper we are exploring the fundamental theory of beamforming, adaptive beamforming technique and tried to implement one of the adaptive algorithm called Least Mean Square algorithm using Xilinx system generator.The compact structure of FPGA beamformer can thus be implemented on any of the Xilinx FPGA using the generated VHDL code.
Predictive LMS for Mobile Channel Tracking
Qassim Nasir,S. Faisal Ali Shah
Journal of Applied Sciences , 2005,
Abstract: The aim of this study was to improve the performance of Least Mean Square (LMS) adaptive algorithm used for fading channel estimation. One step Least Square prediction, based on the estimate of the sampled impulse response and the estimate of their speed of variation, is used along with LMS. The efficiency of the algorithm is confirmed by simulation results for slow, moderate and fast varying mobile channel. The results show about 3 to 11 dB improvement in the Mean Square Deviation between the estimated taps and the actual ones depending on the speed of channel time variations. Pedestrian, slow and fast Vehicular channels with doppler frequencies 6, 100 and 222 Hz, respectively, are used in these tests.
Golden Research Thoughts , 2013, DOI: 10.9780/22315063
Abstract: In numerous applications of signal processing, communications and biomedical we are faced with the necessity to remove noise and distortion from the signals. Adaptive filtering is one of the most important areas in digital signal processing to remove background noise and distortion. As received signal is continuously corrupted by noise where both received signal and noise signal both changes continuously, then this arise the need of adaptive filtering. In last few years various adaptive algorithms are developed for noise cancellation. The normalized least mean square (NLMS) algorithm is an important variant of the classical LMS algorithm for adaptive linear filtering. It possesses many advantages over the LMS algorithm, including having a faster convergence and providing for an automatic time-varying choice of the LMS stepsize parameter that affects the stability, steady-state mean square error (MSE), and convergence speed of the algorithm. An auxiliary fixed step-size that is often introduced in the NLMS algorithmhas the advantage that its stability region (step-size range for algorithm stability) is independent of the signal statistics.This paper describes the development of an adaptive noise cancellation algorithm like NLMS(Normalized Least Mean Square)for effective recognition of signal on MATLAB platform .We simulate the adaptive filter in MATLAB with noisy signal and obtained result shows that NLMS algorithm eliminates noise from noisy signal and get desired result at the output.
Performance Comparison of Adaptive and Blind Equalization Algorithms for Wireless Communication
K. Suthendran
Bonfring International Journal of Research in Communication Engineering , 2013, DOI: 10.9756/bijrce.10063
Abstract: Adaptive equalization is a well known method to minimize the Inter-Symbol Interference (ISI) in wireless communication. Often, adaptive algorithm requires transmission of known training sequence to track the time varying characteristics of the channel and hence utilizes additional bandwidth. It is also impractical to have training sequences in all types of transmissions (e.g. non-cooperative environment). Blind algorithm is a concept to track the time varying characteristics of the channel in the absence of training sequence. However, it leads to slow convergence. In this paper, we compare the performance of adaptive LMS algorithm and SATO based blind algorithm for PAM signal.
A Joint WLMS Channel Estimation and Detection Algorithm Based on Turbo Iteration in OFDM Systems

Bai Bin-feng,Cai Yue-ming,Xu Xin,

电子与信息学报 , 2007,
Abstract: A joint WLMS channel estimation and detection algorithm based Turbo iteration is proposed in this paper. Using the soft information provided by the soft mapping and demapping algorithms, the proposed algorithm achieve its information exchange between detection module and channel estimation module. The channel response is updated symbol by symbol by using the WLMS channel estimation and tracking algorithm. The simulation results show that the BER performance had converged after two iterative, and there is only 0.4-0.5dB performance losing compared with ideal channel estimation at a system BER of10^-3 .

He Hanxiang,Yin Junxun,Ouyang Jingzheng,

电子与信息学报 , 1998,
Abstract: In this paper, the updating equations of FBLMS and UFBLMS algorithms are transformed into the time domain, then two algorithms(FOBA and UFOBA) are presented by employing time-varying convergence factors which are optimized in a least-square(LS) sense respectively. Although FOBA and UFOBA algorithms require a relatively modest increase in computation for each block iteration compared to FBLMS and UFBLMS algorithms respectively, it is shown by simulations that these-algorithms improve convergence speed and accucary of adaptation.
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